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Machine Learning: End-to-End guide for Java developers

You're reading from   Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

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Product type Course
Published in Oct 2017
Publisher Packt
ISBN-13 9781788622219
Length 1159 pages
Edition 1st Edition
Languages
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Authors (2):
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Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
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Toc

What you need for this learning path

Module 1:

Many of the examples in this module use Java 8 features. There are a number of Java APIs demonstrated, each of which is introduced before it is applied. An IDE is not required but is desirable.

Module 2:

To follow the examples throughout the module, you'll need a personal computer with the JDK installed. All the examples and source code that you can download assume Eclipse IDE with support for Maven, a dependency management and build automation tool; and Git, a version control system. Examples in the chapters rely on various libraries, including Weka, deeplearning4j, Mallet, and Apache Mahout. Instructions on how to get and install the libraries are provided in the chapter where the library will be first used.

The module has a dedicated web site, http://machine-learning-in-java.com, where you can find all the example code, errata, and additional materials that will help you to get started.

Module 3:

This book assumes you have some experience of programming in Java and a basic understanding of machine learning concepts. If that doesn't apply to you, but you are curious nonetheless and self-motivated, fret not, and read on! For those who do have some background, it means that you are familiar with simple statistical analysis of data and concepts involved in supervised and unsupervised learning. Those who may not have the requisite math or must poke the far reaches of their memory to shake loose the odd formula or funny symbol, do not be disheartened. If you are the sort that loves a challenge, the short primer in the appendices may be all you need to kick-start your engines—a bit of tenacity will see you through the rest! For those who have never been introduced to machine learning, the first chapter was equally written for you as for those needing a refresher—it is your starter-kit to jump in feet first and find out what it's all about. You can augment your basics with any number of online resources. Finally, for those innocent of Java, here's a secret: many of the tools featured in the book have powerful GUIs. Some include wizard-like interfaces, making them quite easy to use, and do not require any knowledge of Java. So if you are new to Java, just skip the examples that need coding and learn to use the GUI-based tools instead!

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